Instructions to use magicslabnu/clip_OutEffHop_vit_small_patch16_224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use magicslabnu/clip_OutEffHop_vit_small_patch16_224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="magicslabnu/clip_OutEffHop_vit_small_patch16_224", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("magicslabnu/clip_OutEffHop_vit_small_patch16_224", trust_remote_code=True) model = AutoModelForImageClassification.from_pretrained("magicslabnu/clip_OutEffHop_vit_small_patch16_224", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +9 -2
config.json
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],
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"attention_probs_dropout_prob": 0.0,
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"encoder_stride": 16,
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"hidden_act": "gelu"
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"hidden_dropout_prob": 0.0,
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"hidden_size": 384,
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"id2label": {
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"patch_size": 16,
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"qkv_bias": true,
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"torch_dtype": "float32",
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"transformers_version": "4.41.2"
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}
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],
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"attention_probs_dropout_prob": 0.0,
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"encoder_stride": 16,
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"hidden_act": "gelu"
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,
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"hidden_dropout_prob": 0.0,
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"hidden_size": 384,
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"id2label": {
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"patch_size": 16,
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"qkv_bias": true,
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"torch_dtype": "float32",
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"transformers_version": "4.41.2",
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"auto_map":
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{"AutoConfig": "configuration_vit.ViTConfig",
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"AutoModel": "modeling_vit.ViTModel",
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"AutoModelForImageClassification": "modeling_vit.ViTForImageClassification"
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}
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}
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